stepwise_param¶
stepwise_param
¶
Classes¶
StepwiseParam (BaseParam)
¶
Define stepwise params
Parameters¶
{"AIC", "BIC"}, default: 'AIC'
Specify which model selection criterion to be used
{"Hetero", "Homo"}, default: 'Hetero'
Indicate what mode is current task
{"Guest", "Host", "Arbiter"}, default: 'Guest'
Indicate what role is current party
{"both", "forward", "backward"}, default: 'both'
Indicate which direction to go for stepwise. 'forward' means forward selection; 'backward' means elimination; 'both' means possible models of both directions are examined at each step.
int, default: '10'
Specify total number of steps to run before forced stop.
int, default: '2'
Specify the min subset size of final model, cannot be lower than 2. When nvmin > 2, the final model size may be smaller than nvmin due to max_step limit.
int, default: None
Specify the max subset size of final model, 2 <= nvmin <= nvmax. The final model size may be larger than nvmax due to max_step limit.
bool, default False
Indicate if this module needed to be run
Source code in federatedml/param/stepwise_param.py
class StepwiseParam(BaseParam):
"""
Define stepwise params
Parameters
----------
score_name: {"AIC", "BIC"}, default: 'AIC'
Specify which model selection criterion to be used
mode: {"Hetero", "Homo"}, default: 'Hetero'
Indicate what mode is current task
role: {"Guest", "Host", "Arbiter"}, default: 'Guest'
Indicate what role is current party
direction: {"both", "forward", "backward"}, default: 'both'
Indicate which direction to go for stepwise.
'forward' means forward selection; 'backward' means elimination; 'both' means possible models of both directions are examined at each step.
max_step: int, default: '10'
Specify total number of steps to run before forced stop.
nvmin: int, default: '2'
Specify the min subset size of final model, cannot be lower than 2. When nvmin > 2, the final model size may be smaller than nvmin due to max_step limit.
nvmax: int, default: None
Specify the max subset size of final model, 2 <= nvmin <= nvmax. The final model size may be larger than nvmax due to max_step limit.
need_stepwise: bool, default False
Indicate if this module needed to be run
"""
def __init__(self, score_name="AIC", mode=consts.HETERO, role=consts.GUEST, direction="both",
max_step=10, nvmin=2, nvmax=None, need_stepwise=False):
super(StepwiseParam, self).__init__()
self.score_name = score_name
self.mode = mode
self.role = role
self.direction = direction
self.max_step = max_step
self.nvmin = nvmin
self.nvmax = nvmax
self.need_stepwise = need_stepwise
def check(self):
model_param_descr = "stepwise param's"
self.score_name = self.check_and_change_lower(self.score_name, ["aic", "bic"], model_param_descr)
self.check_valid_value(self.mode, model_param_descr, valid_values=[consts.HOMO, consts.HETERO])
self.check_valid_value(self.role, model_param_descr, valid_values=[consts.HOST, consts.GUEST, consts.ARBITER])
self.direction = self.check_and_change_lower(self.direction, ["forward", "backward", "both"], model_param_descr)
self.check_positive_integer(self.max_step, model_param_descr)
self.check_positive_integer(self.nvmin, model_param_descr)
if self.nvmin < 2:
raise ValueError(model_param_descr + " nvmin must be no less than 2.")
if self.nvmax is not None:
self.check_positive_integer(self.nvmax, model_param_descr)
if self.nvmin > self.nvmax:
raise ValueError(model_param_descr + " nvmax must be greater than nvmin.")
self.check_boolean(self.need_stepwise, model_param_descr)
__init__(self, score_name='AIC', mode='hetero', role='guest', direction='both', max_step=10, nvmin=2, nvmax=None, need_stepwise=False)
special
¶
Source code in federatedml/param/stepwise_param.py
def __init__(self, score_name="AIC", mode=consts.HETERO, role=consts.GUEST, direction="both",
max_step=10, nvmin=2, nvmax=None, need_stepwise=False):
super(StepwiseParam, self).__init__()
self.score_name = score_name
self.mode = mode
self.role = role
self.direction = direction
self.max_step = max_step
self.nvmin = nvmin
self.nvmax = nvmax
self.need_stepwise = need_stepwise
check(self)
¶
Source code in federatedml/param/stepwise_param.py
def check(self):
model_param_descr = "stepwise param's"
self.score_name = self.check_and_change_lower(self.score_name, ["aic", "bic"], model_param_descr)
self.check_valid_value(self.mode, model_param_descr, valid_values=[consts.HOMO, consts.HETERO])
self.check_valid_value(self.role, model_param_descr, valid_values=[consts.HOST, consts.GUEST, consts.ARBITER])
self.direction = self.check_and_change_lower(self.direction, ["forward", "backward", "both"], model_param_descr)
self.check_positive_integer(self.max_step, model_param_descr)
self.check_positive_integer(self.nvmin, model_param_descr)
if self.nvmin < 2:
raise ValueError(model_param_descr + " nvmin must be no less than 2.")
if self.nvmax is not None:
self.check_positive_integer(self.nvmax, model_param_descr)
if self.nvmin > self.nvmax:
raise ValueError(model_param_descr + " nvmax must be greater than nvmin.")
self.check_boolean(self.need_stepwise, model_param_descr)